The North American Breeding Bird Survey (BBS) provides data that can be used in complex, multiscale analyses of population change, while controlling for scale‐specific nuisance factors. Many alternative models can be fit to the data, but most model selection procedures are not appropriate for hierarchical models. Leave‐one‐out cross‐validation (LOOCV), in which relative model fit is assessed by omitting an observation and assessing the prediction of a model fit using the remainder of the data, provides a reasonable approach for assessing models, but is time consuming and not feasible to apply for all observations in large data sets. We report the first large‐scale formal model selection for BBS data, applying LOOCV to stratified random samples of observations from BBS data. Our results are for 548 species of North American birds, comparing the fit of four alternative models that differ in year effect structures and in descriptions of extra‐Poisson overdispersion. We use a hierarchical model among species to evaluate posterior probabilities that models are best for individual species. Models in which differences in year effects are conditionally independent (D models) were generally favored over models in which year effects are modeled by a slope parameter and a random year effect (S models), and models in which extra‐Poisson overdispersion effects are independent and t‐distributed (H models) tended to be favored over models where overdispersion was independent and normally distributed. Our conclusions lead us to recommend a change from the conventional S model to D and H models for the vast majority of species (544/548). Comparison of estimated population trends based on the favored model relative to the S model currently used for BBS summaries indicates no consistent differences in estimated trends. Of the 18 species that showed large differences in estimated trends between models, estimated trends from the default S model were more extreme, reflecting the influence of the slope parameter in that model for species that are undergoing large population changes. WAIC, a computationally simpler alternative to LOOCV, does not appear to be a reliable alternative to LOOCV.

中文翻译：

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北美种禽调查的模型选择。

北美种禽调查（BBS）提供的数据可用于复杂，多尺度的种群变化分析，同时还能控制特定于尺度的滋扰因素。许多替代模型都可以适合数据，但是大多数模型选择过程都不适合分层模型。留一法交叉验证（LOOCV），其中通过省略观察并使用剩余数据评估模型拟合的预测来评估相对模型拟合，这提供了一种评估模型的合理方法，但是很费时间并且不适用于大数据集中的所有观测值。我们报告了BBS数据的首次大规模正式模型选择，将LOOCV应用于来自BBS数据的分层观测随机样本。我们的结果是针对548种北美鸟类，比较了四种不同模型的拟合度，这些模型的年效应结构和对泊松超分散的描述不同。我们使用物种之间的分层模型来评估模型最适合单个物种的后验概率。与通过斜率参数和随机年效应模拟年效应的模型（S模型）以及泊松超分散效应是独立的模型相比，通常倾向于使用年效应差异为条件独立的模型（D模型）和t分布（H模型）倾向于优于过度分散是独立且正态分布的模型。我们的结论使我们建议对绝大多数物种（544/548）从传统的S模型改为D和H模型。与当前用于BBS摘要的S模型相比，基于偏爱模型的估计人口趋势的比较表明，估计趋势没有一致的差异。在模型之间的估计趋势存在较大差异的18个物种中，来自默认S模型的估计趋势更为极端，反映了该模型中的坡度参数对种群变化较大的物种的影响。WAIC是LOOCV的一种计算简单的替代方案，似乎不是LOOCV的可靠替代方案。